Appendix 12: Connections

Philosophy

Artificial Intelligence researchers are like the blind men who
went to see the elephant. Having of necessity experienced only tiny
portions of the situation, each comes to a different conclusion about the
nature of the whole.

One group suggests that the construction of an intelligent machine
is very like mathematics, and finding the “theorems” of intelligence will
involve clever representations, transformation rules and long lemmas,
gotten at mostly by thinking hard.

Another school feels AI is like theoretical physics, and the
solution involves finding the universal “laws of intelligence”, by means
of theories guided by experiment.

Yet another sees AI as a little like biology, the idea being to
explain intrinsically complicated natural mechanisms as simply as
possible.

A fourth treats AI as a problem in psychological introspection,
transferring rules of conscious thinking into mechanical form.

A fifth feels the problem is one of engineering, with an
artificial intelligence being just another big machine, to be built
subsystem by subsystem, by rule of thumb and experience based intuition.

People's points of view change with experience and mood, and most
of us have found ourselves espousing different approaches at different
times.

The AI effort has a specific and lofty goal, the matching of human
performance in intellectual and other tasks by machines. Because the
overall goal is still far from accomplished, many of us suffer from doubts
about our progress. These often expresses themselves in the feeling that
much of our field is somehow not “scientific”. Depending on our mood, this
transforms to “not like mathematics”, or "not like physics" etc.. And of
course its easy to find many projects that fail to meet our arbitrary
standards, and confirm our suspicions.

The hard sciences are distinguished from many other intellectual
pursuits not by the quality of the workers, or even the methods employed,
but by the amount of independent verification and refutation practiced.
It is the ruthlessness of the evaluation function that separates the
useless from the valuable and the capable from the incompetent.

I feel it is too early to commit ourselves to or to excessively
condemn any of the various approaches. We ought to judge AI programs on
the basis of performance. Whether or not they conform to our theory of
the moment as to what constitutes intelligence and how to go about
building it, or what is esthetic, we should ask “how well does it work?”.

In other words, I think AI is very like evolution. We should try
different modifications and approaches and see which ones prove themselves
experimentally.

Since this is in itself a prejudgement, I don't really want to
force it on others. But if we suspend disbelief for a few minutes, I can
use it to show why roving vehicles are on the direct path to human
equivalence. The argument is by analogy with natural evolution.

Locomotion, Vision and Intelligence

Consider that, with few exceptions, the only natural systems
with AIish capabilities are large mobile animals. An apparent minimum
size for nerve cells explains the complexity limits on small animals
like insects. The role of mobility in the development of imaging vision
and intelligence is more subtle, yet real. No plants or sessile animals
(what few there are) have imaging eyes or complex nervous systems, but
there are several independent instances of vision and comparative
intelligence in the presence of mobility.

The evolutionary mainstream (as defined by us mainstreamers),
fishes through amphibians and reptiles to mammals to us, is one such
instance. Imaging eyes and a moderate brain developed roughly
simultaneously with a backbone, in motile protofish, sometime in the
Paleozoic, about 450 million years ago. Brain size changed little
through the slow moving amphibian and reptile stages, then accelerated
sharply with the transition to the more mobile mammalian form, about
100 million years ago.

Instance two is the birds, who also have reptilian ancestry,
and who's development parallels our own. Though size limited by the
dynamics of flying, several bird species can match the intellectual
performance of all but the smartest mammals. The battle of wits between
farmers and crows is legendary, and well documented. The intuitive
number sense of these birds goes to seven, compared to three or four
with us (without counting). Hard evidence comes from “reversal
learning” experiments. The response giving the reward in a Skinner box
is occasionally inverted. Most animals are confused by the switch, and
actually take longer than the first time to learn the new state (as if
they first had to unlearn the old rules). Primates (monkeys and apes
and us) among mammals, and virtually all birds, on the other hand,
“catch on” after the first reversal, and react correctly almost
instantly on later swaps.
Instance three is surprising. Most molluscs are nearly blind,
intellectually unimpressive, very slow moving shellfish. Their
relatives who opted for mobility, the cephalopods (octopus and squid)
provide a dramatic contrast, having speed, good eyes, a large brain, a
color display skin, mammal-like behavior, and even manipulators. The
similarities to mammals are especially significant because they were
independently evolved. Our last shared ancestor was a billion year old
bilaterally symmetric pre-worm, with a few neurons. The differences are
interesting. The eyes are hemispherical and firmly attached to the
surrounding skin, and the light sensitive cells in the retina point
outwards, towards the lens. The brain is annular, encircling the
esophagus, and is organized into several connected clumps of ganglia,
one for each arm. A Cousteau film documents an octopus' response to a
“monkey and bananas” problem. A fishbowl sealed
with a large cork,
and containing a small lobster, is dropped into the water near the
animal. The octopus is immediately attracted, seemingly recognizing the
food by sight. It spends a while probing the container and attempting
to reach the lobster from various angles, unsuccessfully. Then,
apparently purposefully, it wraps three or four tentacles around the
bowl, and one about the cork, and pulls. The cork comes free and shoots
to the surface, and the octopus reaches a free tentacle into the bowl
to retrieve the lobster, and eats.

The Point

The point of the preceeding ramble is; moving through the wide
world is a demanding task, and encourages development of complex
responses in those who undertake it. Moving organisms (and machines)
must learn to deal with a wide variety of situations, and have many
responses open to them. This variety places a great premium on general
techniques, and makes highly specialized methods, which may be optimal
for sessile creatures, less valuable. These forces seem to have led to
relative intelligence in animals. Perhaps they mark one route to the
same goal for machines.